Supplementary dataset for "Classification and Biological Identity of Complex Nano Shapes" - Communications Materials Paper (https://doi.org/10.1038/s43246-020-0033-2)

<b>Dataset associated with the paper "Classification and Biological Identity of Complex Nano Shapes"</b>This repository contains data reported in "Classification and Biological Identity of Complex Nano Shapes" by L. Boselli <i>et al.</i> (2020). The main data consist of TEM images and related extracted xy contours coordinates for different shape of NPs. The files containing contours have the extension .dat and consist of two columns with the x and y coordinates of the contours. Additionally we include the code used to extract the contours from the TEM images (directory Contours_extraction_example). An updated version of the codes used to perform the principal components analysis and calculate shape distributions (latest versions labelled 1.1 throughout, published April 2021). Finally, we include data in .xls format related to other graphs in paper (with reference to the specific figures).<b>Important note re previous versions: Versions 1 and 2 of this Figshare data deposit contain older versions of the codes. They should NOT be used to reproduce the figures in the related manuscript nor to produce results.</b>The notation used to name the different folders corresponds to the figures reported in the paper. For example, Figure2_GNP1-4_contours contains the contours used in the calculations to produce Figure 2. As mentioned above, we also include the codes used for the different calculations and a protocol to run these codes (see also the pdf file Protocol_readme). A detailed description of the folders is given below.<b>Notice that these codes are distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.</b><br><b>Contours_extraction_example</b>EXAMPLE OF CONTOUR EXTRACTION FROM TEM IMAGES: the folder contains a TEM_input subfolder where the .tiff TEM images can be loaded and a contour_output subfolder where the file.dat of the contours will be generated by running the following script:<b>$PATH/COMM_MAT/contours_extraction_example$ python get_cont_auto.py TEM_input/ contours_output/ 161 1 16 50 5000 5000</b>where $PATH is the location of the COMM_MAT folder.<br><b>Figure2_GNP1-4_TEMimages and Figure2_GNP1-4_contours</b>SHAPE GROUP ASSIGNMENT FROM EXTRACTED CONTOURS (generation of Graphs similar to Figure 2): scatter plots of GNP1-4 shape groups for the first two and three principal components (PCs) clustering using center of gravity method; Normalized probability distribution of shape variability for each shape group. Notice that the code provided performs the PC analysis only using the magnitude of the Fourier coefficients (see the main text of the manuscripts for more details on the PC analysis). For this reason, the produced PC figures are not exactly the same ones as in the manuscript.The folder Figure2_GNP1-4_contours contains all the relevant contours file divided by subfolder for each GNP batch. The subfolders name will be the one of the legend in the plots. To generate the plots enter the folder and run the script FT_analysis_v1.1.py, typing:&gt; python FT_analysis_v1.1.pyThe script reproduce_fig2_d.py calculates the distribution of aspect ratios and compactness as described in Figure 2.d. To run the code, type the following:&gt; python reproduce_fig2_d.pyThe folders containing the contours for GNP1 and GNP2 must be named GNP1 and GNP2, respectively and must be in the same directory where the program is.Note that colours might be different.<br><b>Figure3_GNP1_3a_3b_4_contours</b>CHARACTERIZATION OF GNPS USED IN THE BIOLOGICAL STUDY (generation of Graphs as in Figure 3.b-d).The folders contains all the relevant contours files divided by subfolder for each GNP batch. The subfolder name will be the one of the legend in the plots. To generate the plots enter the folder and run the script: FT_immuno_v1.1.py with the command python FT_immuno_v1.1.py.Note that colours might be different.<br><b>Figure3_GNP1_3a_3b_4_DCS_ABS</b>CHARACTERIZATION OF GNPS USED IN THE BIOLOGICAL STUDY (generation of Graphs as in Figure 3.e-f).The folder contains data related to DCS and UV-Vis absorption spectra.<br><b>Figure3_GNP3b1_3b2_3b3_contours</b>CHARACTERIZATION OF GNPS REPRODUCIBILITY (generation of Graphs as in Figure 3.g-j).The folder contains all the relevant contours files divided by subfolder for each GNP batch. The subfolder name will be the one of the legend in the plots. To generate the plots enter the folder and run the script: FT_analysis_v1.1.py with the command python FT_analysis_v1.1.py.<br><b>Figure3_GNP3b1_3b2_3b3_DCS_ABS</b>CHARACTERIZATION OF GNPS REPRODUCIBILITY (generation of Graphs as in Figure 3.k-l).The folder contains data related to DCS and UV-Vis absorption spectra.<br><b>Figure4</b>File Fig4a. Heatmap analysis: the heatmap was generated by Morpheus (https://software.broadinstitute.org/morpheus).The analysis is based on Hierarchical Clustering with linkage method of “Average”. The value for each row is transformed by subtract row mean divided by row standard deviation.Principal Coordinates Analysis was performed by open-source R-project (https://www.r-project.org). The code used for the PCoA is:&gt; #calculate the distance between samples<br>&gt; distance &lt;- vegdist(Pep, method = "bray")&gt; # rank samples according to PCoA&gt; pcoa &lt;- cmdscale( distance, k = nrow(Pep)-1, eig = TRUE)&gt; #check the result with a simple picture&gt; ordiplot(scores(pcoa)[ ,c(1, 2)], type = 't')Files Fig4c. GNP1 DEG and Fig4d. GNP3b DEG are referred to the Volcano plots on Fig4.c-d.The Gene ontology (GO) enrichment analysis was performed by Metascape (http://metascape.org/gp/index.html#/main/step1). The analysis was performed by the default settings of “Express Analysis”.<br><b>SF11-13</b>The folder contains data related to LAL assay and DCS of GNP1, GNP3a, GNP3b, and GNP4.